mirror of
https://github.com/infiniflow/ragflow.git
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6e9691a419
### What problem does this PR solve? Manage message and use in agent. Issue #4213 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
454 lines
18 KiB
Python
454 lines
18 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import os
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import re
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import json
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import time
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from abc import abstractmethod
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import infinity
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from infinity.common import ConflictType
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from infinity.index import IndexInfo, IndexType
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from infinity.errors import ErrorCode
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import pandas as pd
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from common.file_utils import get_project_base_directory
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from rag.nlp import is_english
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from common import settings
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from common.doc_store.doc_store_base import DocStoreConnection, MatchExpr, OrderByExpr
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class InfinityConnectionBase(DocStoreConnection):
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def __init__(self, mapping_file_name: str="infinity_mapping.json", logger_name: str="ragflow.infinity_conn"):
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from common.doc_store.infinity_conn_pool import INFINITY_CONN
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self.dbName = settings.INFINITY.get("db_name", "default_db")
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self.mapping_file_name = mapping_file_name
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self.logger = logging.getLogger(logger_name)
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infinity_uri = settings.INFINITY["uri"]
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if ":" in infinity_uri:
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host, port = infinity_uri.split(":")
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infinity_uri = infinity.common.NetworkAddress(host, int(port))
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self.connPool = None
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self.logger.info(f"Use Infinity {infinity_uri} as the doc engine.")
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conn_pool = INFINITY_CONN.get_conn_pool()
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for _ in range(24):
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try:
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inf_conn = conn_pool.get_conn()
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res = inf_conn.show_current_node()
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if res.error_code == ErrorCode.OK and res.server_status in ["started", "alive"]:
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self._migrate_db(inf_conn)
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self.connPool = conn_pool
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conn_pool.release_conn(inf_conn)
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break
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conn_pool.release_conn(inf_conn)
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self.logger.warning(f"Infinity status: {res.server_status}. Waiting Infinity {infinity_uri} to be healthy.")
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time.sleep(5)
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except Exception as e:
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conn_pool = INFINITY_CONN.refresh_conn_pool()
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self.logger.warning(f"{str(e)}. Waiting Infinity {infinity_uri} to be healthy.")
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time.sleep(5)
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if self.connPool is None:
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msg = f"Infinity {infinity_uri} is unhealthy in 120s."
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self.logger.error(msg)
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raise Exception(msg)
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self.logger.info(f"Infinity {infinity_uri} is healthy.")
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def _migrate_db(self, inf_conn):
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inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore)
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fp_mapping = os.path.join(get_project_base_directory(), "conf", self.mapping_file_name)
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if not os.path.exists(fp_mapping):
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raise Exception(f"Mapping file not found at {fp_mapping}")
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schema = json.load(open(fp_mapping))
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table_names = inf_db.list_tables().table_names
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for table_name in table_names:
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inf_table = inf_db.get_table(table_name)
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index_names = inf_table.list_indexes().index_names
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if "q_vec_idx" not in index_names:
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# Skip tables not created by me
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continue
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column_names = inf_table.show_columns()["name"]
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column_names = set(column_names)
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for field_name, field_info in schema.items():
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if field_name in column_names:
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continue
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res = inf_table.add_columns({field_name: field_info})
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assert res.error_code == infinity.ErrorCode.OK
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self.logger.info(f"INFINITY added following column to table {table_name}: {field_name} {field_info}")
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if field_info["type"] != "varchar" or "analyzer" not in field_info:
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continue
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analyzers = field_info["analyzer"]
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if isinstance(analyzers, str):
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analyzers = [analyzers]
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for analyzer in analyzers:
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inf_table.create_index(
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f"ft_{re.sub(r'[^a-zA-Z0-9]', '_', field_name)}_{re.sub(r'[^a-zA-Z0-9]', '_', analyzer)}",
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IndexInfo(field_name, IndexType.FullText, {"ANALYZER": analyzer}),
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ConflictType.Ignore,
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)
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"""
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Dataframe and fields convert
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"""
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@staticmethod
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@abstractmethod
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def field_keyword(field_name: str):
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# judge keyword or not, such as "*_kwd" tag-like columns.
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def convert_select_fields(self, output_fields: list[str]) -> list[str]:
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# rm _kwd, _tks, _sm_tks, _with_weight suffix in field name.
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raise NotImplementedError("Not implemented")
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@staticmethod
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@abstractmethod
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def convert_matching_field(field_weight_str: str) -> str:
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# convert matching field to
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raise NotImplementedError("Not implemented")
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@staticmethod
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def list2str(lst: str | list, sep: str = " ") -> str:
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if isinstance(lst, str):
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return lst
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return sep.join(lst)
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def equivalent_condition_to_str(self, condition: dict, table_instance=None) -> str | None:
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assert "_id" not in condition
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columns = {}
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if table_instance:
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for n, ty, de, _ in table_instance.show_columns().rows():
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columns[n] = (ty, de)
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def exists(cln):
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nonlocal columns
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assert cln in columns, f"'{cln}' should be in '{columns}'."
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ty, de = columns[cln]
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if ty.lower().find("cha"):
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if not de:
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de = ""
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return f" {cln}!='{de}' "
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return f"{cln}!={de}"
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cond = list()
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for k, v in condition.items():
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if not isinstance(k, str) or not v:
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continue
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if self.field_keyword(k):
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if isinstance(v, list):
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inCond = list()
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for item in v:
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if isinstance(item, str):
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item = item.replace("'", "''")
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inCond.append(f"filter_fulltext('{self.convert_matching_field(k)}', '{item}')")
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if inCond:
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strInCond = " or ".join(inCond)
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strInCond = f"({strInCond})"
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cond.append(strInCond)
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else:
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cond.append(f"filter_fulltext('{self.convert_matching_field(k)}', '{v}')")
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elif isinstance(v, list):
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inCond = list()
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for item in v:
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if isinstance(item, str):
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item = item.replace("'", "''")
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inCond.append(f"'{item}'")
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else:
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inCond.append(str(item))
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if inCond:
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strInCond = ", ".join(inCond)
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strInCond = f"{k} IN ({strInCond})"
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cond.append(strInCond)
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elif k == "must_not":
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if isinstance(v, dict):
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for kk, vv in v.items():
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if kk == "exists":
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cond.append("NOT (%s)" % exists(vv))
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elif isinstance(v, str):
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cond.append(f"{k}='{v}'")
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elif k == "exists":
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cond.append(exists(v))
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else:
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cond.append(f"{k}={str(v)}")
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return " AND ".join(cond) if cond else "1=1"
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@staticmethod
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def concat_dataframes(df_list: list[pd.DataFrame], select_fields: list[str]) -> pd.DataFrame:
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df_list2 = [df for df in df_list if not df.empty]
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if df_list2:
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return pd.concat(df_list2, axis=0).reset_index(drop=True)
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schema = []
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for field_name in select_fields:
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if field_name == "score()": # Workaround: fix schema is changed to score()
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schema.append("SCORE")
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elif field_name == "similarity()": # Workaround: fix schema is changed to similarity()
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schema.append("SIMILARITY")
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else:
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schema.append(field_name)
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return pd.DataFrame(columns=schema)
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"""
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Database operations
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"""
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def db_type(self) -> str:
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return "infinity"
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def health(self) -> dict:
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"""
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Return the health status of the database.
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"""
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inf_conn = self.connPool.get_conn()
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res = inf_conn.show_current_node()
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self.connPool.release_conn(inf_conn)
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res2 = {
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"type": "infinity",
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"status": "green" if res.error_code == 0 and res.server_status in ["started", "alive"] else "red",
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"error": res.error_msg,
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}
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return res2
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"""
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Table operations
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"""
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def create_idx(self, index_name: str, dataset_id: str, vector_size: int):
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table_name = f"{index_name}_{dataset_id}"
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inf_conn = self.connPool.get_conn()
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inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore)
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fp_mapping = os.path.join(get_project_base_directory(), "conf", self.mapping_file_name)
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if not os.path.exists(fp_mapping):
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raise Exception(f"Mapping file not found at {fp_mapping}")
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schema = json.load(open(fp_mapping))
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vector_name = f"q_{vector_size}_vec"
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schema[vector_name] = {"type": f"vector,{vector_size},float"}
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inf_table = inf_db.create_table(
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table_name,
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schema,
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ConflictType.Ignore,
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)
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inf_table.create_index(
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"q_vec_idx",
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IndexInfo(
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vector_name,
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IndexType.Hnsw,
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{
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"M": "16",
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"ef_construction": "50",
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"metric": "cosine",
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"encode": "lvq",
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},
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),
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ConflictType.Ignore,
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)
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for field_name, field_info in schema.items():
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if field_info["type"] != "varchar" or "analyzer" not in field_info:
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continue
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analyzers = field_info["analyzer"]
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if isinstance(analyzers, str):
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analyzers = [analyzers]
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for analyzer in analyzers:
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inf_table.create_index(
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f"ft_{re.sub(r'[^a-zA-Z0-9]', '_', field_name)}_{re.sub(r'[^a-zA-Z0-9]', '_', analyzer)}",
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IndexInfo(field_name, IndexType.FullText, {"ANALYZER": analyzer}),
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ConflictType.Ignore,
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)
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self.connPool.release_conn(inf_conn)
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self.logger.info(f"INFINITY created table {table_name}, vector size {vector_size}")
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return True
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def delete_idx(self, index_name: str, dataset_id: str):
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table_name = f"{index_name}_{dataset_id}"
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inf_conn = self.connPool.get_conn()
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db_instance = inf_conn.get_database(self.dbName)
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db_instance.drop_table(table_name, ConflictType.Ignore)
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self.connPool.release_conn(inf_conn)
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self.logger.info(f"INFINITY dropped table {table_name}")
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def index_exist(self, index_name: str, dataset_id: str) -> bool:
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table_name = f"{index_name}_{dataset_id}"
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try:
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inf_conn = self.connPool.get_conn()
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db_instance = inf_conn.get_database(self.dbName)
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_ = db_instance.get_table(table_name)
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self.connPool.release_conn(inf_conn)
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return True
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except Exception as e:
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self.logger.warning(f"INFINITY indexExist {str(e)}")
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return False
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"""
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CRUD operations
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"""
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@abstractmethod
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def search(
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self,
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select_fields: list[str],
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highlight_fields: list[str],
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condition: dict,
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match_expressions: list[MatchExpr],
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order_by: OrderByExpr,
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offset: int,
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limit: int,
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index_names: str | list[str],
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dataset_ids: list[str],
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agg_fields: list[str] | None = None,
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rank_feature: dict | None = None,
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) -> tuple[pd.DataFrame, int]:
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def get(self, doc_id: str, index_name: str, knowledgebase_ids: list[str]) -> dict | None:
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def insert(self, documents: list[dict], index_name: str, dataset_ids: str = None) -> list[str]:
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def update(self, condition: dict, new_value: dict, index_name: str, dataset_id: str) -> bool:
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raise NotImplementedError("Not implemented")
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def delete(self, condition: dict, index_name: str, dataset_id: str) -> int:
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inf_conn = self.connPool.get_conn()
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db_instance = inf_conn.get_database(self.dbName)
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table_name = f"{index_name}_{dataset_id}"
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try:
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table_instance = db_instance.get_table(table_name)
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except Exception:
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self.logger.warning(f"Skipped deleting from table {table_name} since the table doesn't exist.")
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return 0
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filter = self.equivalent_condition_to_str(condition, table_instance)
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self.logger.debug(f"INFINITY delete table {table_name}, filter {filter}.")
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res = table_instance.delete(filter)
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self.connPool.release_conn(inf_conn)
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return res.deleted_rows
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"""
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Helper functions for search result
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"""
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def get_total(self, res: tuple[pd.DataFrame, int] | pd.DataFrame) -> int:
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if isinstance(res, tuple):
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return res[1]
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return len(res)
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def get_doc_ids(self, res: tuple[pd.DataFrame, int] | pd.DataFrame) -> list[str]:
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if isinstance(res, tuple):
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res = res[0]
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return list(res["id"])
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@abstractmethod
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def get_fields(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fields: list[str]) -> dict[str, dict]:
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raise NotImplementedError("Not implemented")
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def get_highlight(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, keywords: list[str], field_name: str):
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if isinstance(res, tuple):
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res = res[0]
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ans = {}
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num_rows = len(res)
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column_id = res["id"]
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if field_name not in res:
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return {}
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for i in range(num_rows):
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id = column_id[i]
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txt = res[field_name][i]
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if re.search(r"<em>[^<>]+</em>", txt, flags=re.IGNORECASE | re.MULTILINE):
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ans[id] = txt
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continue
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txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE)
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txt_list = []
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for t in re.split(r"[.?!;\n]", txt):
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if is_english([t]):
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for w in keywords:
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t = re.sub(
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r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w),
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r"\1<em>\2</em>\3",
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t,
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flags=re.IGNORECASE | re.MULTILINE,
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)
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else:
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for w in sorted(keywords, key=len, reverse=True):
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t = re.sub(
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re.escape(w),
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f"<em>{w}</em>",
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t,
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flags=re.IGNORECASE | re.MULTILINE,
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)
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if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE):
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continue
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txt_list.append(t)
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if txt_list:
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ans[id] = "...".join(txt_list)
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else:
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ans[id] = txt
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return ans
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def get_aggregation(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, field_name: str):
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"""
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Manual aggregation for tag fields since Infinity doesn't provide native aggregation
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"""
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from collections import Counter
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# Extract DataFrame from result
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if isinstance(res, tuple):
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df, _ = res
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else:
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df = res
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if df.empty or field_name not in df.columns:
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return []
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# Aggregate tag counts
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tag_counter = Counter()
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for value in df[field_name]:
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if pd.isna(value) or not value:
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continue
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# Handle different tag formats
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if isinstance(value, str):
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# Split by ### for tag_kwd field or comma for other formats
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if field_name == "tag_kwd" and "###" in value:
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tags = [tag.strip() for tag in value.split("###") if tag.strip()]
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else:
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# Try comma separation as fallback
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tags = [tag.strip() for tag in value.split(",") if tag.strip()]
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for tag in tags:
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if tag: # Only count non-empty tags
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tag_counter[tag] += 1
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elif isinstance(value, list):
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# Handle list format
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for tag in value:
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if tag and isinstance(tag, str):
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tag_counter[tag.strip()] += 1
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# Return as list of [tag, count] pairs, sorted by count descending
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return [[tag, count] for tag, count in tag_counter.most_common()]
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"""
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SQL
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"""
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def sql(self, sql: str, fetch_size: int, format: str):
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raise NotImplementedError("Not implemented")
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